Higher UV Index Predicts Slightly Lower Burps for Population

Higher UV Index Predicts Slightly Lower Burps for Population

Contents

Variables

A
UV Index 595
A
Burps 313

Categories

A
Environment 564
A
Symptoms 13336

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 39.4% average decrease in Burps following above average UV Index.

Abstract

Burps was generally 45.8% lower than average after 5.92 index of UV Index per 24 hours.

Aggregated data from 1 study participants suggests with a HIGH degree of confidence (p=0.001, 95% CI -0.304 to 0.076) that UV Index has a weakly negative predictive relationship (R=-0.114) with Burps.

The highest quartile of Burps measurements were observed following an average 4.45 index UV Index.

The lowest quartile of Burps measurements were observed following an average 5.9 index of UV Index.

After an onset delay of 0 seconds, Burps is typically 1% lower than average over the 24 hours following around 5.9 index of UV Index UV Index.

Keywords: UV Index, Burps, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 1 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 1,428 paired observations from 1 participants revealed a substantial reduction in Burps following above-average UV Index exposure.

-45.8%
Change from Baseline
Substantial effect on Burps
0.01
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.114
Correlation (r)
p = 1.000
Significance
z = 0.19
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average UV Index:

  • Burps decreased by 45.8% on average
  • Temporal analysis supports UV Index as the predictor (not the outcome)

Interpreting the Predictor Impact Score

The Predictor Impact Score (PIS) integrates multiple Bradford Hill causal criteria into a single metric. Use this guide to interpret the score:

PIS Range Interpretation Recommended Action
≥ 0.5 Strong evidence High priority for RCT validation
0.3 - 0.5 Moderate evidence Consider for experimental investigation
0.1 - 0.3 Weak evidence Monitor for additional data
< 0.1 Insufficient evidence Low priority; may be noise

Note: PIS is a prioritization heuristic, not proof of causation. High scores indicate relationships worth investigating, not confirmed causal effects. With only 1 participants, these scores are preliminary and will become more reliable as additional data is collected.

Optimal Daily Values (Precision Dosing)

Based on the observed relationship, we can estimate the predictor values associated with the best and worst outcomes. These values enable personalized dosing recommendations.

⚠️ Preliminary Data: With 1 participants and 1,428 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

4.4 index
Value Predicting Higher Burps
Average UV Index when Burps exceeded its mean
5.9 index
Value Predicting Lower Burps
Average UV Index when Burps was below its mean

What This Suggests

Burps tended to be lowest (best) when UV Index was around 5.9 index.

Important: These values reflect correlations, not guaranteed causal effects. Individual responses may vary. Use as a starting point for personal experimentation, not as a definitive prescription. Consult healthcare providers before making treatment decisions.

Population Correlation

UV Index Distribution

Burps Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name UV Index
Effect Variable Name Burps
Sinn Predictive Coefficient 0.010819985800734
Confidence Level HIGH
Confidence Interval 0.18980851154517
Forward Pearson Predictive Coefficient -0.1137
Critical T Value 1.646
Average UV Index Over Previous 24 hours Before ABOVE Average Burps 4.45 index
Average UV Index Over Previous 24 hours Before BELOW Average Burps 5.9 index
Duration of Action 24 hours
Effect Size weakly negative
Number of Paired Measurements 1428
Optimal Pearson Product 0.053543953084823
P Value 0.001
Statistical Significance 1
Strength of Relationship 0.18980851154517
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

UV Index Info

Property Value
Variable Name UV Index
Aggregation Method MEAN
Analysis Performed At 2022-08-10
Duration of Action 24 hours
Kurtosis 1.822010060732
Mean 5.9878842271663 index
Median 5.9006088992974 index
Minimum Allowed Value 0 index
Number of Aggregate Predictors 32
Number of Aggregate Outcomes 563
Number of Measurements 2808
Number of Measurements (including those generated by tagged, joined, or child variables) 2808
Public true
Onset Delay 0 seconds
Standard Deviation 2.4107553795229
Unit Index
User Variables 1004
UPC 0
Variable Category Environment
Variable ID 5974181
Variance 6.7284308269145

Burps Info

Property Value
Variable Name Burps
Aggregation Method SUM
Analysis Performed At 2022-11-18
Duration of Action 24 hours
Filling Value 0
Kurtosis 24.099671000076
Mean 0.56 count
Median 0 count
Minimum Allowed Value 0 count
Number of Aggregate Predictors 230
Number of Aggregate Outcomes 83
Number of Measurements 1407
Number of Measurements (including those generated by tagged, joined, or child variables) 1407
Public true
Onset Delay 0 seconds
Standard Deviation 1.8368177544746
Unit Count
User Variables 1
UPC 841908100511
Variable Category Symptoms
Variable ID 5967916
Variance 3.373899463153

Introduction

Background

UV Index (Environment) and Burps (Symptoms) are both important factors in understanding human health and well-being. This study investigates the relationship between these two variables using real-world observational data.

Traditional randomized controlled trials (RCTs), while the gold standard for causal inference, are often impractical, expensive, or unethical for studying many health relationships. Aggregated N-of-1 observational studies offer a complementary approach that leverages within-subject comparisons across large populations to identify meaningful patterns.

Research Question

Does UV Index affect Burps?

Additionally, we seek to determine:

  1. What is the direction and magnitude of any effect?
  2. How confident can we be in this relationship based on the available data?
  3. What are the optimal levels of UV Index for maximizing Burps?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between UV Index and Burps. Additionally, we attempt to determine the UV Index values most likely to produce optimal Burps values.

Study Overview

This is a population-level observational study using aggregated N-of-1 methodology. By aggregating individual N-of-1 experiments, we can identify population-level patterns while accounting for the substantial individual variation that exists in most health relationships. Effect sizes are reported as percent change from baseline, enabling intuitive interpretation and comparison across different measures.

Full Methodology: Framework for Real-World Evidence-Based Pharmacovigilance: Aggregated N-of-1 Trials for Quantifying Treatment Effects

Discussion

Interpretation of Findings

Participants experienced a 45.8% reduction in Burps following above-average UV Index exposure. The Predictor Impact Score (PIS) of 0.01 indicates insufficient evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Burps is statistically significant at a 95% confidence interval. The p-value of 1.0000 indicates there is less than a 100.00% probability that this result occurred by chance.

After treatment, a 39.4% decrease (-0.462 count) from the mean baseline 1.01 count was observed. The relative standard deviation at baseline was 238.3%. The observed change was 0.19 times the standard deviation.

A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).

T-Test Details
Observed t-value: 4.695
Critical t-value: 1.646

Since t = 4.70 > 1.65, we reject the null hypothesis.

Biological Plausibility

A plausible bio-chemical mechanism between predictor and outcome is critical for interpreting observational findings. This is where human judgment excels beyond statistical analysis.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which UV Index might influence Burps.

Bradford Hill Criteria Assessment

The Bradford Hill criteria provide a framework for assessing causality in observational studies. Our methodology operationalizes six of the nine criteria through the Predictor Impact Score (PIS):

Criterion How Addressed Metric
Strength Effect size magnitude Percent change from baseline (Δ%), z-score
Consistency Cross-participant replication Number of users (N), number of pairs (n)
Temporality Predictor precedes outcome Temporality factor (φ), onset delay (δ > 0)
Biological Gradient Dose-response relationship Gradient coefficient (φgradient)
Plausibility Biological mechanism assessment Community votes on mechanism plausibility
Specificity Category appropriateness Interest factor (finterest)

Predictor Impact Score (PIS)

The PIS integrates multiple Bradford Hill criteria into a composite metric quantifying how reliably a predictor affects an outcome. Higher scores indicate stronger evidence:

Population-Level PIS:

$$\text{PIS}_{\text{agg}} = |r_{\text{forward}}| \cdot w \cdot \phi_{\text{users}} \cdot \phi_{\text{pairs}} \cdot \phi_{\text{change}} \cdot \phi_{\text{gradient}}$$

Where φ-factors are saturation functions approaching 1 as evidence accumulates:

  • φusers = 1 - e-N/10 (user saturation)
  • φpairs = 1 - e-n/nsig (pair saturation)
  • φchange = 1 - e-Δspread/Δsig (effect spread saturation)
  • w = weighted average of plausibility votes

Temporality Assessment

We assess evidence for correct causal direction using the temporality factor:

$$\phi_{\text{temporal}} = \frac{|r_{\text{forward}}|}{|r_{\text{forward}}| + |r_{\text{reverse}}|}$$

Values approaching 1 indicate the predictor precedes the outcome (supporting causation); values near 0.5 suggest ambiguous directionality; values near 0 suggest reverse causation or confounding by indication.

Limitations

As with any observational study, correlation does not prove causation. Key limitations include:

  • Unmeasured confounders: Variables not tracked may influence results
  • Self-selection bias: Health trackers may differ from the general population
  • Measurement error: Self-reported data may contain recall bias
  • Confounding by indication: Sicker individuals may use more treatments

However, within-subject comparison and temporal precedence analysis partially mitigate these limitations. If the relationship is merely coincidental, as participants independently modify their UV Index values, the observed strength will decline over time. Spurious correlations naturally dissipate as more data is collected.

Future Directions

Future research should examine:

  • Subgroup analyses to identify individual differences in response
  • Potential confounders and mediators of the observed relationship
  • Optimal dosing and timing for UV Index
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 1 participants, these results are based on limited data. Effect sizes and confidence will improve as more participants contribute data. Consider these findings directional rather than definitive.

Above-average UV Index was associated with a 45.8% reduction in Burps—a substantial effect. The Predictor Impact Score of 0.01 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.01 and a 45.8% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how UV Index may influence Burps in real-world conditions. While preliminary, these results may inform future research directions. As more participants contribute data, the reliability and precision of these findings will improve substantially.

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Methods

Study Design

This study is based on data donated by 1 participants. Thus, the study design is equivalent to the aggregation of 1 separate n=1 observational natural experiments.

This within-subject design is powerful because it controls for all stable individual characteristics (genetics, baseline health status, socioeconomic factors) that might otherwise confound the relationship between variables.

Data Analysis

Temporal Assumptions

The analysis incorporates temporal assumptions about the relationship between variables:

  • Onset Delay: It was assumed that 0 seconds would pass before a change in UV Index would produce an observable change in Burps.
  • Duration of Action: It was assumed that UV Index could produce an observable change in Burps for as much as 24 hours after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between UV Index values and subsequent Burps values. Individual correlations were then aggregated using Fisher's z-transformation to produce a population-level estimate:

Individual Correlation:

$$r_i = \frac{\sum(x_{ij} - \bar{x}_i)(y_{ij} - \bar{y}_i)}{\sqrt{\sum(x_{ij} - \bar{x}_i)^2 \sum(y_{ij} - \bar{y}_i)^2}}$$

Fisher's Z-Transformation:

$$z_i = \frac{1}{2} \ln\left(\frac{1 + r_i}{1 - r_i}\right)$$

Aggregated Correlation:

$$\bar{r} = \tanh(\bar{z}) \quad \text{where} \quad \bar{z} = \frac{1}{N}\sum_{i=1}^{N} z_i$$

Effect Size Calculation

Effect sizes are reported as percent change from baseline. For each participant, we compare the outcome following above-average predictor values to the overall baseline outcome:

$$\Delta\%_{\text{baseline}} = \frac{\bar{O}_{\text{follow-up}} - \bar{O}_{\text{baseline}}}{\bar{O}_{\text{baseline}}} \times 100$$

Effect Magnitude (Z-Score)

To assess effect magnitude relative to natural variability, we calculate the z-score:

$$z = \frac{|\Delta\%_{\text{baseline}}|}{\text{RSD}_{\text{baseline}}}$$

where RSDbaseline is the relative standard deviation of outcome during baseline period

A z-score > 2 indicates statistical significance (p < 0.05), meaning the observed change exceeds typical baseline fluctuation and is unlikely due to random variation.

Statistical Significance

Correlation significance is assessed using a two-tailed t-test:

$$t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}$$

We reject the null hypothesis (ρ = 0) at α = 0.05 when |t| exceeds the critical value, providing statistical evidence that the observed relationship is not due to chance.

Data Sources

UV Index data was primarily collected using Weather. Automatically import temperature, humidity, and ultraviolet light exposure.

Burps data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Data Quality

Data quality measures were applied to ensure reliable results:

  • Minimum Data Requirement: Only participants with sufficient paired observations were included in the analysis.
  • Outlier Handling: Extreme values were winsorized to reduce the influence of measurement errors.
  • Missing Data: Days with missing values were handled using appropriate filling strategies based on the variable type.
  • Test User Exclusion: Test accounts and invalid users were excluded from all analyses.

Principal Investigator

Program & Methods

Mike P. Sinn
Sinn, M. P.

Designed and implemented data collection, aggregation, causal inference pipeline, and automated study generation framework. Developed the Predictor Impact Score methodology operationalizing Bradford Hill criteria for ranking causal relationships in observational data. When he tells people this at parties, they usually say they have to go check on their car.

Individual study outputs are automated, reproducible, and open to external audit. (Which I would seriously recommend.)

Contact

Cite This Study

APA Format
Sinn, M. P. (2026). Causal Analysis: Does UV Index Affect Burps?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5974181-effect-5967916-population-study
BibTeX 
@misc{sinn_cause_5974181_effect_5967916_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does UV Index Affect Burps?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5974181-effect-5967916-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does UV Index Affect Burps?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-5974181-effect-5967916-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does UV Index Affect Burps?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5974181-effect-5967916-population-study [Accessed January 10, 2026].

Study Type: Aggregated N-of-1 Observational Mega-Study
Evidence Level: Level II (Real-World Evidence)
Methodology: Bradford Hill Criteria with Predictor Impact Score (PIS)

References

This framework was originally developed in 2013 based on the Bradford Hill criteria. Subsequent literature has independently validated similar approaches to causal inference from observational data:

  1. Hill, A.B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. [Bradford Hill criteria]
  2. Lillie, E.O., et al. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161-173. [N-of-1 methodology]
  3. Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
  4. Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
  5. FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
  6. Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
  7. Platt, R., et al. (2018). The FDA Sentinel Initiative—an evolving national resource . New England Journal of Medicine, 379(22), 2091-2093.

This information is for research and educational purposes only, not medical advice. Consult a healthcare provider before making health decisions. Terms of Service

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